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[Hardware] Valid 1Z0-1122-25 Exam Duration - 1Z0-1122-25 Certification Materials

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【Hardware】 Valid 1Z0-1122-25 Exam Duration - 1Z0-1122-25 Certification Materials

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Oracle Cloud Infrastructure 2025 AI Foundations Associate Sample Questions (Q19-Q24):NEW QUESTION # 19
What is the difference between classification and regression in Supervised Machine Learning?
  • A. Classification and regression both assign data points to categories.
  • B. Classification and regression both predict continuous values.
  • C. Classification assigns data points to categories, whereas regression predicts continuous values.
  • D. Classification predicts continuous values, whereas regression assigns data points to categories.
Answer: C
Explanation:
In supervised machine learning, the key difference between classification and regression lies in the nature of the output they predict. Classification algorithms are used to assign data points to one of several predefined categories or classes, making it suitable for tasks like spam detection, where an email is classified as either "spam" or "not spam." On the other hand, regression algorithms predict continuous values, such as forecasting the price of a house based on features like size, location, and number of rooms. While classification answers "which category?" regression answers "how much?" or "what value?".

NEW QUESTION # 20
You are working on a project for a healthcare organization that wants to develop a system to predict the severity of patients' illnesses upon admission to a hospital. The goal is to classify patients into three categories - Low Risk, Moderate Risk, and High Risk - based on their medical history and vital signs. Which type of supervised learning algorithm is required in this scenario?
  • A. Clustering
  • B. Multi-Class Classification
  • C. Regression
  • D. Binary Classification
Answer: B
Explanation:
In this healthcare scenario, where the goal is to classify patients into three categories-Low Risk, Moderate Risk, and High Risk-based on their medical history and vital signs, a Multi-Class Classification algorithm is required. Multi-class classification is a type of supervised learning algorithm used when there are three or more classes or categories to predict. This method is well-suited for situations where each instance needs to be classified into one of several categories, which aligns with the requirement to categorize patients into different risk levels.

NEW QUESTION # 21
Which statement describes the Optical Character Recognition (OCR) feature of Oracle Cloud Infrastructure Document Understanding?
  • A. It converts audio files into text.
  • B. It enhances the visual quality of documents.
  • C. It recognizes and extracts text from a document.
  • D. It provides real-time translation of text.
Answer: C
Explanation:
The Optical Character Recognition (OCR) feature of Oracle Cloud Infrastructure (OCI) Document Understanding recognizes and extracts text from documents. This capability is fundamental for converting printed or handwritten text into a machine-readable format, allowing for further processing, such as text analysis, search, and archiving. OCI's OCR is an essential tool in automating document processing workflows, enabling businesses to digitize and manage their documents efficiently.

NEW QUESTION # 22
Which type of machine learning is used to understand relationships within data and is not focused on making predictions or classifications?
  • A. Unsupervised learning
  • B. Reinforcement learning
  • C. Active learning
  • D. Supervised learning
Answer: A
Explanation:
Unsupervised learning is a type of machine learning that focuses on understanding relationships within data without the need for labeled outcomes. Unlike supervised learning, which requires labeled data to train models to make predictions or classifications, unsupervised learning works with unlabeled data and aims to discover hidden patterns, groupings, or structures within the data.
Common applications of unsupervised learning include clustering, where the algorithm groups data points into clusters based on similarities, and association, where it identifies relationships between variables in the dataset. Since unsupervised learning does not predict outcomes but rather uncovers inherent structures, it is ideal for exploratory data analysis and discovering previously unknown patterns in data .

NEW QUESTION # 23
Which AI Ethics principle leads to the Responsible AI requirement of transparency?
  • A. Respect for human autonomy
  • B. Explicability
  • C. Prevention of harm
  • D. Fairness
Answer: B
Explanation:
Explicability is the AI Ethics principle that leads to the Responsible AI requirement of transparency. This principle emphasizes the importance of making AI systems understandable and interpretable to humans. Transparency is a key aspect of explicability, as it ensures that the decision-making processes of AI systems are clear and comprehensible, allowing users to understand how and why a particular decision or output was generated. This is critical for building trust in AI systems and ensuring that they are used responsibly and ethically.
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